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20202022
most citedNovel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection

1 citations · 1 across the 2 of their papers we have counts for

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cs.CL20221 cited

Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection

Hardy Hardy, Miguel Ballesteros, Faisal Ladhak +3

Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout…

cs.CL2022

Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents

Muhammad Khalifa, Yogarshi Vyas, Shuai Wang +3

We investigate semi-structured document classification in a zero-shot setting. Classification of semi-structured documents is more challenging than that of standard unstructured do…

cs.CL2021

A Bag of Tricks for Dialogue Summarization

Muhammad Khalifa, Miguel Ballesteros, Kathleen McKeown

Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the tas…

cs.CL2021

Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling

Muhammad Khalifa, Muhammad Abdul-Mageed, Khaled Shaalan

A sufficient amount of annotated data is usually required to fine-tune pre-trained language models for downstream tasks. Unfortunately, attaining labeled data can be costly, especi…

cs.CL2020

A Distributional Approach to Controlled Text Generation

Muhammad Khalifa, Hady Elsahar, Marc Dymetman

We propose a Distributional Approach for addressing Controlled Text Generation from pre-trained Language Models (LMs). This approach permits to specify, in a single formal framewor…